{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目5. 寿命拟合\n",
    "\n",
    "- 姓名：杜东洋\n",
    "- 学号：20201050438\n",
    "- 邮箱：Starry_339@163.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目介绍\n",
    "\n",
    "我们的世界多姿多彩，变化万千。但如果你仔细观察，能够发现更为有趣的现象。\n",
    "\n",
    "本项目聚焦光电过程。光电过程是一个光子-电子和电子-光子转化的过程，稍瞬即逝。好在科学家和工程师们开发了先进的技术，可以利用超快探测器或者相机捕获这些过程。\n",
    "\n",
    "- 现假设有一个发光染料，其由两种发光物质组成，比例分别为A和B。\n",
    "\n",
    "- 这两种发光物质分别具有不同的激子寿命（即电子从高能级跃迁到低能级的平均所用的时间）\n",
    "\n",
    "- 每一种发光物质如果单独被一个超短的激光激发，该物质的发光强度随时间的变化过程可以表示为：\n",
    "\n",
    "$$I(t)=I(0)e^{-\\frac{t}{\\tau}}$$\n",
    "\n",
    "- 当上述染料被激发时，其内的两种发光物质同时被激发，且初始发光强度与物质的比例成正比，因此，总的强度可以表示为：\n",
    "\n",
    "$$I(t)=Ae^{-\\frac{t}{\\tau_1}} + Be^{-\\frac{t}{\\tau_2}}$$\n",
    "\n",
    "- 该发光过程我们用一个超快探测器进行探测，测得的电信号（如光电流）与光信号成正比，因此该光电流的变化过程也符合上面的公式。\n",
    "\n",
    "- 假设实验记录了时间在$0-1\\mu s$内的光电流值，采样间隔为2ns。\n",
    "\n",
    "\n",
    "## 项目目的\n",
    "本项目需要结合我们学习过的非线性最小二乘法拟合得到未知参数：$A, B, \\tau_1, \\tau_2$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高斯-牛顿法介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设实验测得一组数据：\n",
    "$(x_1, y_1), (x_2, y_2),... ...,(x_n, y_n)$\n",
    "\n",
    "我们期望用某一个函数去拟合这些数据，得到最小二乘下的最优近似。\n",
    "\n",
    "假设该函数包含m个待拟合参数：$c_1, c_2, ... ..., c_m$。该函数可以表示为：\n",
    "\n",
    "$y = f(x, c_1, c_2, ... ..., c_m)$\n",
    "\n",
    "则在每一个已知数据点下，对于给定的某一组$c_1, c_2, ... ..., c_m$可以求得对应的误差值：\n",
    "\n",
    "$$\n",
    "E_1(c_1, c_2, ... ..., c_m) = f(x_1, c_1, c_2, ... ..., c_m) - y_1 \\\\\n",
    "E_2(c_1, c_2, ... ..., c_m) = f(x_2, c_1, c_2, ... ..., c_m) - y_2 \\\\\n",
    "E_3(c_1, c_2, ... ..., c_m) = f(x_3, c_1, c_2, ... ..., c_m) - y_3 \\\\\n",
    "... ... \\\\\n",
    "E_n(c_1, c_2, ... ..., c_m) = f(x_n, c_1, c_2, ... ..., c_m) - y_1 \\\\\n",
    "$$\n",
    "\n",
    "高斯-牛顿迭代法的公式如下：\n",
    "\n",
    "$$\n",
    "<c_1, c_2, ... ..., c_m>_{i+1} = <c_1, c_2, ... ..., c_m>_{i} - \n",
    "\\frac{{DF(<c_1, c_2, ... ..., c_m>_{i})}^TF(<c_1, c_2, ... ..., c_m>_{i})}{{DF(<c_1, c_2, ... ..., c_m>_{i})}^T{DF(<c_1, c_2, ... ..., c_m>_{i})}}\n",
    "$$\n",
    "\n",
    "其中：\n",
    "\n",
    "$$\n",
    "F(c_1, c_2, ... ..., c_m) =\n",
    "\\left[\n",
    "\\begin{matrix}\n",
    "E_1(c_1, c_2, ... ..., c_m) \\\\\n",
    "E_2(c_1, c_2, ... ..., c_m) \\\\\n",
    "E_3(c_1, c_2, ... ..., c_m) \\\\\n",
    "... ... \\\\\n",
    "E_n(c_1, c_2, ... ..., c_m)\n",
    "\\end{matrix}\n",
    "\\right]\n",
    "$$\n",
    "\n",
    "\n",
    "$$\n",
    "DF(c_1, c_2, ... ..., c_m) =\n",
    "\\left[\n",
    "\\begin{matrix}\n",
    "\\frac{\\partial{E_1}}{\\partial{c_1}} & \\frac{\\partial{E_1}}{\\partial{c_2}} & ... & \\frac{\\partial{E_1}}{\\partial{c_m}}\\\\\n",
    "\\frac{\\partial{E_2}}{\\partial{c_1}} & \\frac{\\partial{E_2}}{\\partial{c_2}} & ... & \\frac{\\partial{E_2}}{\\partial{c_m}}\\\\\n",
    "\\frac{\\partial{E_3}}{\\partial{c_1}} & \\frac{\\partial{E_3}}{\\partial{c_2}} & ... & \\frac{\\partial{E_3}}{\\partial{c_m}}\\\\\n",
    "... & ... & ... & ... \\\\\n",
    "\\frac{\\partial{E_n}}{\\partial{c_1}} & \\frac{\\partial{E_1}}{\\partial{c_2}} & ... & \\frac{\\partial{E_1}}{\\partial{c_m}}\\\\\n",
    "\\end{matrix}\n",
    "\\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 模拟实验过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 定义光电流的时间依赖函数f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 补全以下代码，使函数f计算得到某一个时刻t的光电流值\n",
    "# 假设采用的参数分别为A，B，t1，t2\n",
    "# 其中A，B，t1，t2由一个numpy向量C来表示\n",
    "# 即C=[A，B，t1，t2]\n",
    "\n",
    "def f(t, C):\n",
    "    # 补全下面的return命令\n",
    "    return C[0]*np.exp(-t/C[2]) + C[1]*np.exp(-t/C[3])   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 测试f函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库函数\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.9999999399999995\n"
     ]
    }
   ],
   "source": [
    "# 随便定义一个C\n",
    "# 在下面[]内填入[A，B，t1，t2]\n",
    "C = [2,4,100000000,100000000]\n",
    "C = np.array(C)\n",
    "print(f(1, C))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 准备实验数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自己设定一个A，B，t1，t2来初始化下面的C\n",
    "\n",
    "# 设置A，范围最好在1-10\n",
    "A = 5\n",
    "\n",
    "# 设置A，范围最好在1-10\n",
    "B = 7\n",
    "\n",
    "# 设置t1，范围最好在10-500纳秒\n",
    "t1 = 400\n",
    "\n",
    "# 设置t2，范围最好在10-500纳秒\n",
    "t2 = 350\n",
    "\n",
    "# 将上面数据组合成一个向量C\n",
    "C = np.array([A, B, t1, t2])\n",
    "\n",
    "# 设置一个噪声强度，范围在0.01 - 0.5之间\n",
    "noiseLevel = 0.01\n",
    "\n",
    "# 定义实验的时间\n",
    "timeStart = 0             # 纳秒\n",
    "timeStop = 2000           # 纳秒\n",
    "timeStep = 2              # 纳秒\n",
    "\n",
    "# 计算t向量\n",
    "t = np.arange(timeStart, timeStop, timeStep)\n",
    "\n",
    "# 调用f函数，并加上一定的噪声，从而模拟测得的光电流向量\n",
    "I = f(t, C) + (np.random.rand(t.size)  - 0.5) * noiseLevel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 做图展示该实验数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.026800675339574802, 14.397736247202438)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(t, I, c = 'r')\n",
    "plt.xlabel(\"time (ns)\", fontsize = 20)\n",
    "plt.ylabel(\"Light intensity\", fontsize = 20)\n",
    "plt.yscale(\"log\")\n",
    "plt.xlim(- timeStop * 0.02, timeStop * 1.02)\n",
    "plt.ylim(np.abs(np.min(I)) * 0.5, np.max(I) * 1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 高斯-牛顿法最小二乘拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 定义F(X)函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def F(X):\n",
    "    return f(t, X) - I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.88646066e-03 -4.95629272e-03 -9.60285451e-04 -5.02270791e-05\n",
      "  7.54182272e-04  4.04467256e-03  7.21966197e-04  3.34525536e-03\n",
      " -2.94074364e-03 -1.01895032e-03 -3.66412689e-03 -2.62747531e-03\n",
      " -4.65648494e-03 -2.37294016e-03 -2.25496891e-03 -4.54776391e-03\n",
      "  1.32752060e-03  2.81153652e-03  3.45732812e-03  3.02894638e-03]\n"
     ]
    }
   ],
   "source": [
    "# 测试F(X)\n",
    "# 打印F(X)的前二十行\n",
    "print(F(C)[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 定义DF(X)函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 此处采用一阶差分近似导数\n",
    "def DF(X):\n",
    "    dx = 0.001\n",
    "    dX = np.array([dx, dx, dx, dx])\n",
    "    # 初始化一个DF矩阵，具有t维度的行数和C维度的列数\n",
    "    result = np.zeros((t.size,X.size))\n",
    "    \n",
    "    # 进行循环计算\n",
    "    # 补全下面的DF(X)计算过程\n",
    "    # 建议采用差分代替导数\n",
    "    # python自带的库中有exp函数\n",
    "    for i in range(t.size):\n",
    "        result[i,0] = np.exp(-t[i]/X[2])\n",
    "        result[i,1] = np.exp(-t[i]/X[3])\n",
    "        result[i,2] = X[0]*t[i]*np.exp(-t[i]/X[2])/X[2]**2\n",
    "        result[i,3] = X[1]*t[i]*np.exp(-t[i]/X[3])/X[3]**2\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [9.95012479e-01, 9.94302010e-01, 6.21882799e-05, 1.13634515e-04],\n",
       "       [9.90049834e-01, 9.88636487e-01, 1.23756229e-04, 2.25974054e-04],\n",
       "       [9.85111940e-01, 9.83003246e-01, 1.84708489e-04, 3.37029684e-04],\n",
       "       [9.80198673e-01, 9.77402103e-01, 2.45049668e-04, 4.46812390e-04],\n",
       "       [9.75309912e-01, 9.71832875e-01, 3.04784348e-04, 5.55333071e-04],\n",
       "       [9.70445534e-01, 9.66295381e-01, 3.63917075e-04, 6.62602547e-04],\n",
       "       [9.65605416e-01, 9.60789439e-01, 4.22452370e-04, 7.68631551e-04],\n",
       "       [9.60789439e-01, 9.55314870e-01, 4.80394720e-04, 8.73430739e-04],\n",
       "       [9.55997482e-01, 9.49871496e-01, 5.37748584e-04, 9.77010681e-04],\n",
       "       [9.51229425e-01, 9.44459137e-01, 5.94518390e-04, 1.07938187e-03],\n",
       "       [9.46485148e-01, 9.39077618e-01, 6.50708539e-04, 1.18055472e-03],\n",
       "       [9.41764534e-01, 9.33726763e-01, 7.06323400e-04, 1.28053956e-03],\n",
       "       [9.37067463e-01, 9.28406397e-01, 7.61367314e-04, 1.37934665e-03],\n",
       "       [9.32393820e-01, 9.23116346e-01, 8.15844592e-04, 1.47698615e-03],\n",
       "       [9.27743486e-01, 9.17856438e-01, 8.69759518e-04, 1.57346818e-03],\n",
       "       [9.23116346e-01, 9.12626501e-01, 9.23116346e-04, 1.66880275e-03],\n",
       "       [9.18512284e-01, 9.07426365e-01, 9.75919302e-04, 1.76299979e-03],\n",
       "       [9.13931185e-01, 9.02255858e-01, 1.02817258e-03, 1.85606919e-03],\n",
       "       [9.09372934e-01, 8.97114813e-01, 1.07988036e-03, 1.94802074e-03]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试DF(X)\n",
    "# 打印DF(X)的前二十行\n",
    "DF(C)[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 迭代求解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 1 次迭代：  [1.96047883e-01 1.16451220e+01 1.00009813e+01 4.99998533e+02]\n",
      "第 2 次迭代：  [1.96049053e-01 1.16451192e+01 6.00593032e+01 3.72743549e+02]\n",
      "第 3 次迭代：  [5.33656365e-02 1.19411667e+01 7.83849219e+01 3.71851348e+02]\n",
      "第 4 次迭代：  [7.51521168e-02 1.19204420e+01 1.71392981e+02 3.72217459e+02]\n",
      "第 5 次迭代：  [3.03287132e-01 1.16954973e+01 3.85129542e+02 3.74564358e+02]\n",
      "第 6 次迭代：  [ 139.61642133 -127.61618587 -862.31329889  276.96716908]\n",
      "第 7 次迭代：  [ 9.10152732e-02  1.17837355e+01 -8.62565331e+02  2.69458892e+02]\n",
      "第 8 次迭代：  [ 1.06332631e-01  1.17425753e+01 -1.31619516e+03  3.56235988e+02]\n",
      "第 9 次迭代：  [ 1.25980583e-02  1.19820306e+01 -1.41669973e+03  3.69545499e+02]\n",
      "第 10 次迭代：  [ 1.03649459e-02  1.19857143e+01 -2.21897044e+03  3.69601688e+02]\n",
      "第 11 次迭代：  [ 1.51757712e-02  1.19814759e+01 -5.66166223e+03  3.69404668e+02]\n",
      "第 12 次迭代：  [ 2.28795188e-02  1.19743620e+01 -2.81788946e+04  3.69152331e+02]\n",
      "第 13 次迭代：  [ 2.83070028e-02  1.19692253e+01 -4.78793035e+05  3.69002223e+02]\n",
      "第 14 次迭代：  [ 2.97767077e-02  1.19678233e+01 -1.10639250e+08  3.68964330e+02]\n",
      "第 15 次迭代：  [ 2.98711981e-02  1.19677331e+01 -5.60840891e+12  3.68961916e+02]\n",
      "第 16 次迭代：  [ 2.98716416e-02  1.19677327e+01 -1.43655445e+22  3.68961904e+02]\n",
      "第 17 次迭代：  [ 2.98716418e-02  1.19677327e+01 -9.42498121e+40  3.68961904e+02]\n",
      "第 18 次迭代：  [ 2.98716418e-02  1.19677327e+01 -4.05692791e+78  3.68961904e+02]\n",
      "第 19 次迭代：  [         inf         -inf         -inf 368.96190412]\n",
      "第 20 次迭代：  [nan nan nan nan]\n",
      "第 21 次迭代：  [nan nan nan nan]\n",
      "第 22 次迭代：  [nan nan nan nan]\n",
      "第 23 次迭代：  [nan nan nan nan]\n",
      "第 24 次迭代：  [nan nan nan nan]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\AppData\\Local\\Temp/ipykernel_13300/1523150647.py:15: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  result[i,2] = X[0]*t[i]*np.exp(-t[i]/X[2])/X[2]**2\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Temp/ipykernel_13300/1523150647.py:16: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  result[i,3] = X[1]*t[i]*np.exp(-t[i]/X[3])/X[3]**2\n",
      "C:\\Users\\lenovo\\AppData\\Local\\Temp/ipykernel_13300/28155794.py:8: RuntimeWarning: invalid value encountered in add\n",
      "  return C[0]*np.exp(-t/C[2]) + C[1]*np.exp(-t/C[3])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 25 次迭代：  [nan nan nan nan]\n",
      "第 26 次迭代：  [nan nan nan nan]\n",
      "第 27 次迭代：  [nan nan nan nan]\n",
      "第 28 次迭代：  [nan nan nan nan]\n",
      "第 29 次迭代：  [nan nan nan nan]\n",
      "第 30 次迭代：  [nan nan nan nan]\n",
      "第 31 次迭代：  [nan nan nan nan]\n",
      "第 32 次迭代：  [nan nan nan nan]\n",
      "第 33 次迭代：  [nan nan nan nan]\n",
      "第 34 次迭代：  [nan nan nan nan]\n",
      "第 35 次迭代：  [nan nan nan nan]\n",
      "第 36 次迭代：  [nan nan nan nan]\n",
      "第 37 次迭代：  [nan nan nan nan]\n",
      "第 38 次迭代：  [nan nan nan nan]\n",
      "第 39 次迭代：  [nan nan nan nan]\n",
      "第 40 次迭代：  [nan nan nan nan]\n",
      "第 41 次迭代：  [nan nan nan nan]\n",
      "第 42 次迭代：  [nan nan nan nan]\n",
      "第 43 次迭代：  [nan nan nan nan]\n",
      "第 44 次迭代：  [nan nan nan nan]\n",
      "第 45 次迭代：  [nan nan nan nan]\n",
      "第 46 次迭代：  [nan nan nan nan]\n",
      "第 47 次迭代：  [nan nan nan nan]\n",
      "第 48 次迭代：  [nan nan nan nan]\n",
      "第 49 次迭代：  [nan nan nan nan]\n",
      "第 50 次迭代：  [nan nan nan nan]\n",
      "第 51 次迭代：  [nan nan nan nan]\n",
      "第 52 次迭代：  [nan nan nan nan]\n",
      "第 53 次迭代：  [nan nan nan nan]\n",
      "第 54 次迭代：  [nan nan nan nan]\n",
      "第 55 次迭代：  [nan nan nan nan]\n",
      "第 56 次迭代：  [nan nan nan nan]\n",
      "第 57 次迭代：  [nan nan nan nan]\n",
      "第 58 次迭代：  [nan nan nan nan]\n",
      "第 59 次迭代：  [nan nan nan nan]\n",
      "第 60 次迭代：  [nan nan nan nan]\n",
      "第 61 次迭代：  [nan nan nan nan]\n",
      "第 62 次迭代：  [nan nan nan nan]\n",
      "第 63 次迭代：  [nan nan nan nan]\n",
      "第 64 次迭代：  [nan nan nan nan]\n",
      "第 65 次迭代：  [nan nan nan nan]\n",
      "第 66 次迭代：  [nan nan nan nan]\n",
      "第 67 次迭代：  [nan nan nan nan]\n",
      "第 68 次迭代：  [nan nan nan nan]\n",
      "第 69 次迭代：  [nan nan nan nan]\n",
      "第 70 次迭代：  [nan nan nan nan]\n",
      "第 71 次迭代：  [nan nan nan nan]\n",
      "第 72 次迭代：  [nan nan nan nan]\n",
      "第 73 次迭代：  [nan nan nan nan]\n",
      "第 74 次迭代：  [nan nan nan nan]\n",
      "第 75 次迭代：  [nan nan nan nan]\n",
      "第 76 次迭代：  [nan nan nan nan]\n",
      "第 77 次迭代：  [nan nan nan nan]\n",
      "第 78 次迭代：  [nan nan nan nan]\n",
      "第 79 次迭代：  [nan nan nan nan]\n",
      "第 80 次迭代：  [nan nan nan nan]\n",
      "第 81 次迭代：  [nan nan nan nan]\n",
      "第 82 次迭代：  [nan nan nan nan]\n",
      "第 83 次迭代：  [nan nan nan nan]\n",
      "第 84 次迭代：  [nan nan nan nan]\n",
      "第 85 次迭代：  [nan nan nan nan]\n",
      "第 86 次迭代：  [nan nan nan nan]\n",
      "第 87 次迭代：  [nan nan nan nan]\n",
      "第 88 次迭代：  [nan nan nan nan]\n",
      "第 89 次迭代：  [nan nan nan nan]\n",
      "第 90 次迭代：  [nan nan nan nan]\n",
      "第 91 次迭代：  [nan nan nan nan]\n",
      "第 92 次迭代：  [nan nan nan nan]\n",
      "第 93 次迭代：  [nan nan nan nan]\n",
      "第 94 次迭代：  [nan nan nan nan]\n",
      "第 95 次迭代：  [nan nan nan nan]\n",
      "第 96 次迭代：  [nan nan nan nan]\n",
      "第 97 次迭代：  [nan nan nan nan]\n",
      "第 98 次迭代：  [nan nan nan nan]\n",
      "第 99 次迭代：  [nan nan nan nan]\n",
      "第 100 次迭代：  [nan nan nan nan]\n"
     ]
    }
   ],
   "source": [
    "# 给出C的初始猜测，按照[A, B, t1, t2]格式\n",
    "C_guess = [10000,1010000,10,500]  ## 给出初始值\n",
    "\n",
    "C_fitting = [C_guess]\n",
    "\n",
    "# 给出计算终止条件\n",
    "error = 1e-3\n",
    "\n",
    "# 给出最大迭代次数\n",
    "maxIter = 100\n",
    "\n",
    "for i in range(maxIter):\n",
    "    # 获取当前的C，即Xold\n",
    "    Ci = np.array(C_fitting[i])\n",
    "    DFi = DF(Ci)\n",
    "    Fi = F(Ci)\n",
    "    \n",
    "    # 这里展示利用python里面的try... except...语句进行异常处理\n",
    "    try:\n",
    "        Cnext = Ci - np.linalg.inv(DFi.T @ DFi) @ (DFi.T @ Fi)\n",
    "    except:\n",
    "        print(\"\\n计算不收敛，重新给出一个初始猜测\")\n",
    "        break\n",
    "        \n",
    "    C_fitting.append(list(Cnext))\n",
    "    print('第', i+1, '次迭代： ',Cnext)\n",
    "    \n",
    "    # 判断是否达到误差要求\n",
    "    # 这里利用内积法计算误差\n",
    "    currentError = (Cnext - Ci).T @ (Cnext - Ci)\n",
    "    if currentError < error:\n",
    "        print(\"\\n经过\", i, \"次迭代，拟合成功!\")\n",
    "        print(\"\\n结果是:\\t\\t\", Cnext)\n",
    "        print(\"\\n理论值是：\\t\", C)\n",
    "        break   \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 做图展示拟合过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.xlabel(\"time (ns)\", fontsize = 20)\n",
    "plt.ylabel(\"Light intensity\", fontsize = 20)\n",
    "\n",
    "plt.xlim(- timeStop * 0.02, timeStop * 1.02)\n",
    "plt.ylim(np.abs(np.min(I)), np.max(I) * 1.2)\n",
    "\n",
    "plt.scatter(t, I, c = 'k')\n",
    "\n",
    "legend = []\n",
    "for i in range(len(C_fitting)):\n",
    "    I_fitting = f(t, C_fitting[i])\n",
    "    plt.plot(t, I_fitting)\n",
    "    legend.append(i)\n",
    "\n",
    "legend.append(\"raw data\")\n",
    "plt.legend(legend)\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 1 次迭代：  [-237.00110427  244.83783214  830.99686136  483.46046834]\n",
      "第 2 次迭代：  [ 53.26935684 -45.39977073 875.08092037 441.89606648]\n",
      "第 3 次迭代：  [ 21.51450492 -13.62665619 771.61611115 563.45384931]\n",
      "第 4 次迭代：  [  363.95573135  -356.11499043 -1095.78624546  3387.49059125]\n",
      "第 5 次迭代：  [   16.5001857     -9.89315196 -1113.65002818  4268.60877406]\n",
      "第 6 次迭代：  [ 2.04065329e+01 -1.38391054e+01 -1.59957084e+03  5.87154582e+04]\n",
      "第 7 次迭代：  [ 1.30820924e+02 -1.24267443e+02 -5.26106317e+03  1.72458983e+07]\n",
      "第 8 次迭代：  [ 5.83133884e+03 -5.82454920e+03 -1.09411284e+05  1.51421398e+12]\n",
      "第 9 次迭代：  [ 1.08253588e+07 -1.08253485e+07 -1.00483931e+08  1.97048719e+22]\n",
      "第 10 次迭代：  [ 1.41645790e+07 -1.41648759e+07  3.04650536e+11  1.17246158e+40]\n",
      "第 11 次迭代：  [ 1.94953699e+08 -1.94953868e+08  4.43390080e+20  6.56702600e+77]\n",
      "\n",
      "计算不收敛，重新给出一个初始猜测\n"
     ]
    }
   ],
   "source": [
    "# 给出C的初始猜测，按照[A, B, t1, t2]格式\n",
    "C_guess = [10,10,140,100]  ## 给出初始值\n",
    "\n",
    "C_fitting = [C_guess]\n",
    "\n",
    "# 给出计算终止条件\n",
    "error = 1e-3\n",
    "\n",
    "# 给出最大迭代次数\n",
    "maxIter = 100\n",
    "\n",
    "for i in range(maxIter):\n",
    "    # 获取当前的C，即Xold\n",
    "    Ci = np.array(C_fitting[i])\n",
    "    DFi = DF(Ci)\n",
    "    Fi = F(Ci)\n",
    "    \n",
    "    # 这里展示利用python里面的try... except...语句进行异常处理\n",
    "    try:\n",
    "        Cnext = Ci - np.linalg.inv(DFi.T @ DFi) @ (DFi.T @ Fi)\n",
    "    except:\n",
    "        print(\"\\n计算不收敛，重新给出一个初始猜测\")\n",
    "        break\n",
    "        \n",
    "    C_fitting.append(list(Cnext))\n",
    "    print('第', i+1, '次迭代： ',Cnext)\n",
    "    \n",
    "    # 判断是否达到误差要求\n",
    "    # 这里利用内积法计算误差\n",
    "    currentError = (Cnext - Ci).T @ (Cnext - Ci)\n",
    "    if currentError < error:\n",
    "        print(\"\\n经过\", i, \"次迭代，拟合成功!\")\n",
    "        print(\"\\n结果是:\\t\\t\", Cnext)\n",
    "        print(\"\\n理论值是：\\t\", C)\n",
    "        break   \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分享经验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 如果计算过程中出现奇异矩阵很可能也会导致结果不收敛。\n",
    "* 一阶差分近似并不一定比直接求导简单。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
